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This paper challenges the 'Attention-Confidence Assumption' by demonstrating that attention map sharpness is a poor predictor of correctness in Vision-Language Models. Instead, it shows that reliability is better indicated by hidden-state geometry and self-consistency, with significant findings on architectural differences between late-fusion and early-fusion models.